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We strongly suggest creating a compelling title slide for viral attention. YOU MUST USE THE SMX FOOTER ON YOUR TITLE SLIDE!

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- 1. #SMX #21C1 @minderwinter Charles Midwinter, Collegis Education Visualizing Attribution in Living Color
- 2. #SMX #21C1 @minderwinter When multiple channels or tactics assist with a conversion, an attribution model is the set of rules we use to “attribute” portions of the conversion to each assisting touch-point. But you already knew that… What is Attribution (review, obviously)?
- 3. #SMX #21C1 @minderwinter Last Interaction Last Non-direct Click Last AdWords Click First Interaction Linear Time Decay Position Based Google Analytics Attribution Models
- 4. #SMX #21C1 @minderwinter Almost anything is better than “Last Click,” but black boxes aren’t much better. No visibility on the details of the attribution calculation Possible pitfalls with certain channels Too many groundless assumptions required The Problem with Out-of-the-Box Attribution Models
- 5. #SMX #21C1 @minderwinter If you want to understand multi-channel attribution, the “multi-channel attribution funnel” reports in Google Analytics are your first stop. Take a look at the “top conversion paths” report This is great information, but how to summarize it at a high level? Google Analytics & Channel/Tactic Interactions
- 6. #SMX #21C1 @minderwinter The object that can summarize these conversion paths is called an “edge matrix.” Usually used for the analysis of networks (eg. social networks) Encodes the connections among entities Can be visualized as a “node graph” with open source software (Gephi) Edge Matrices
- 7. #SMX #21C1 @minderwinter Consider the following conversion paths: A > C > B > C A > B B > C Edge Matrix Example 1/3
- 8. #SMX #21C1 @minderwinter In words A referred to C once referred to B once B referred to C twice C referred to B once Edge Matrix Example 2/3
- 9. #SMX #21C1 @minderwinter As an “Edge Matrix” Edge Matrix Example 3/3 A B C A 0 1 1 B 0 0 2 C 0 1 0
- 10. #SMX #21C1 @minderwinter Just use my handy dandy Python script. Go to: traffictheory.org/smx-2015 Download the script Make sure you have Python 2.7 installed (not Python 3!) Follow the instructions at the URL above to run. MCF Top Conversion Paths to Edge Matrix
- 11. #SMX #21C1 @minderwinter To visualize the “Edge Matrix” as a Node Graph, you’ll need Gephi, open source graph software. Open the “edge_matrix.csv” file created by the Python script (see website for more details) Import the “last_click.csv” file created by the Python script (see website for more details) Turning an Edge Matrix into a Node Graph
- 12. #SMX #21C1 @minderwinter How do we turn this spaghetti into something useful? The Raw Node Graph
- 13. #SMX #21C1 @minderwinter A layout algorithm uses the weights of the connections/edges to re-arrange the nodes. Usually physics-based, involving a gravitation-like attraction that scales with the edge weights between nodes, and often a repulsion that separates weakly connected nodes. Layout Algorithms
- 14. #SMX #21C1 @minderwinter Nodes that refer to each other often are now placed close together in 2D space. Two central communities of nodes are identifiable (“direct/(none)” and “google/organic”) The Result of Layout Algorithm “Force Atlas 2”
- 15. #SMX #21C1 @minderwinter To make this graph more useful, we’d like to map a metric to node size The metric should give us some indication of the node’s importance to the conversion process In order to proceed, we should understand a bit more about the node graph Measuring Node Importance
- 16. #SMX #21C1 @minderwinter Degree: the number of a node’s connections. In-Degree: the number of a node’s incoming connections Out-Degree: the number of a node’s out- going connections Degree
- 17. #SMX #21C1 @minderwinter A Degree = 2 In-Degree = 0 Out-Degree = 2 Degree Example A B C A 0 1 1 B 0 0 2 C 0 1 0
- 18. #SMX #21C1 @minderwinter B Degree = 1 In-Degree = 0 Out-Degree = 1 Degree Example A B C A 0 1 1 B 0 0 2 C 0 1 0
- 19. #SMX #21C1 @minderwinter Weighted Degree: the number of a node’s connections multiplied by their weights. In-Degree: the number of a node’s incoming connections multiplied by their weights. Out-Degree: the number of a node’s out- going connections multiplied by their weights. Weighted Degree
- 20. #SMX #21C1 @minderwinter B Weighted Degree = 2 In-Degree = 0 Out-Degree = 2 Weighted Degree Example A B C A 0 1 1 B 0 0 2 C 0 1 0
- 21. #SMX #21C1 @minderwinter The most important nodes are the ones generating incremental conversions Conceptually, they generate a net output. A node that gets no in-bound connections, but has many out- bound connections is a source of conversions, and should be highly valued. A node that generates a lot of last-click conversions has value, but its net output should be adjusted so that in-bound connections are subtracted. A node that has as many in-bound connections as it does last- click/out-bound connections is adding little value from an incremental perspective. Assessing Node (Campaign or Source/Medium) Importance
- 22. #SMX #21C1 @minderwinter (Weighted Out-degree + Last Click) – Weighted In-Degree This metric gives us an indication of node importance from an incremental conversion perspective. Net Output
- 23. #SMX #21C1 @minderwinter Nodes that generate more incremental conversions are larger Caveat: flawed tracking means this metric is far from perfect Mapping “Net Output” to Node Size
- 24. #SMX #21C1 @minderwinter Positioning tells us which nodes are closely connected, and size tells us how well nodes generate incremental conversions It would also be nice to know how each node tends to assist in the conversion process: does it produce last clicks, or is it higher in the funnel? Assessing Node Function
- 25. #SMX #21C1 @minderwinter The lower a node is in the conversion funnel, the more last clicks it should have The higher a node is in the funnel, the more likely it is to push traffic to other nodes (high weighted out-degree) Funnel Position 1/2
- 26. #SMX #21C1 @minderwinter Last Click / (Weighted Out-degree + Last Click) 0 for nodes with no last click 1 for nodes with all last click Varies from 0 to 1 as ratio of last click to weighted out-degree increases Funnel Position 2/2
- 27. #SMX #21C1 @minderwinter Nodes high in the funnel are redder Nodes lower in the funnel are bluer In-between nodes are lighter in color, sometimes almost white. Mapping Funnel Position to Node Color
- 28. #SMX #21C1 @minderwinter The Final Result
- 29. #SMX #21C1 @minderwinter Proximity tells you how often channels interact Color tells you a channel/campaign’s position in the funnel Size tells you how many incremental conversions are likely generated by a channel/campaign How to Interpret the Result
- 30. #SMX #21C1 @minderwinter Identify “sinks” Sinks are blueish. These kinds of channels are at the end of the conversion path They are lynch pins in the network, fed by channels higher in the funnel Overvalued by last click Sinks
- 31. #SMX #21C1 @minderwinter Identify “sources”: Reddish Tend to be earlier in the conversion path Undervalued by last click Sources
- 32. #SMX #21C1 @minderwinter Identify “assistors”: Pale, or sometimes white Beware of small assistors Tend to be midway in the conversion path Undervalued by last click, but can be overvalued by other models Assistors
- 33. #SMX #21C1 @minderwinter Display Retargeting Direct Buy Behavioral Paid Search Branded Unbranded Organic Search Referral Social Direct Source, Sink, or Assistor?
- 34. #SMX #21C1 @minderwinter Display Retargeting (Assistor) Direct Buy (Source) Behavioral (Source/Assistor) Paid Search Branded (Sink) Unbranded (Source/Assistor) Organic Search (Assistor/Sink) Referral (Source/Assistor) Social (Assistor) Direct (Assistor/Sink) Source, Sink, or Assistor?
- 35. #SMX #21C1 @minderwinter Depending on your sales cycle, channels & campaigns may function differently in the conversion funnel Results May Vary
- 36. #SMX #21C1 @minderwinter Nodes with little visibility are hard to interpret: Organic: because of (not provided), its a mix of branded and unbranded. Its “Funnel Position” will be determined by the strength of your brand and the amount of unbranded organic traffic you receive. Direct: can skew your results. We know it contains all kinds of poorly tracked traffic. Sometimes, I just go ahead and remove direct from the graph. Caveats
- 37. #SMX #21C1 @minderwinter Select an attribution model that fits your conversion process Sources are under valued by both last click and time decay, for example. Identify outliers and understand what they say about your mix (discover fraud) Use the visualization rhetorically to justify budget for exposure tactics How to Make This Actionable
- 38. #SMX #21C1 @minderwinter THANK YOU! Charles Midwinter Associate Director of Marketing Strategy Collegis Education traffictheory.org/smx-2015

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